Learnable Game Engine for Goal-Driven Scene Control
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Solution Overview
Problem
Existing game engines struggle to accurately model game logic, comprehend the meaning of different parts of game environments, and perform high-level goal-driven control of game flow, especially in complex scenarios like tennis, where fine-grained action representation is lacking and discrete action representations are difficult to define.
Innovation Solution
A Learnable Game Engine (LGE) framework that uses a neural network model trained on annotated videos to understand game environments, learn game AI, and perform high-level action reasoning, incorporating a compositional NeRF representation for rendering and a diffusion-based animation model for generating game sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a continuous action representation is used to model real-world driving scenarios, then the action space becomes more flexible and easier to define, but the obtained continuous action space is high-dimensional and difficult for the user to interact with
Solution Approach 1:
The patent segments the continuous action space into a discrete action representation by learning a discrete action space from annotated videos. This segmentation transforms the high-dimensional continuous action space into a lower-dimensional discrete action space that is easier to interact with while preserving the flexibility needed for complex scenarios like tennis.
Solution Approach 2:
The patent creates a learned discrete action representation that copies the essential characteristics of continuous actions while presenting them in a simplified discrete format. The discrete action labels are learned from annotated video data, capturing the meaningful variations in actions without the complexity of continuous parameter spaces.
2Ease of operation
If a discrete action representation is learned from annotated videos, then the action space becomes easier to interact with, but the model cannot perform constraint- or goal-driven generation for which non-sequential conditioning is necessary
Solution Approach 1:
The patent makes the discrete action representation universal by training it on diverse annotated videos from multiple domains (tennis, Minecraft, driving). This enables the same discrete action labels to be used across different scenarios and domains, providing both ease of interaction and broad adaptability for various generation tasks including goal-driven generation.
Solution Approach 2:
The patent introduces dynamic conditioning capabilities that allow the model to perform non-sequential conditioning and goal-driven generation. The system can condition on future goals and constraints while generating action sequences, transforming the static discrete action representation into a dynamic system capable of handling complex generation tasks.
3Device complexity
If existing approaches perform generation in an auto-regressive manner conditioned on actions, then the generation process is simple, but the approaches are unable to perform constraint- or goal-driven generation
Solution Approach 1:
The patent implements preliminary action by allowing the model to condition on future goals and constraints before generating the action sequence. The system can take goal-driven conditions as input and generate action sequences that satisfy those future constraints, enabling goal-driven generation without significantly complicating the overall generation process.
Data Source
AI summary
A framework trains game-engine-like neural models from annotated videos to generate a Learnable Game Engine (LGE) that maintains states of the scene, objects and agents in it, and enables rendering the environment from a controllable viewpoint. The LGE models the logic of the game and the rules of physics, making it possible for the user to play the game by specifying both high- and low-level action sequences. The LGE also unlocks a director's mode where the game is played by plotting behind the scenes, specifying high-level actions and goals for the agents using text-based instructions. To implement the director's mode, a trained diffusion-based animation model navigates the scene using high-level constraints, to enable play against an adversary, and to devise the strategy to win a point. To render the resulting state of the environment and its agents, a compositional neural radiance field (NeRF) representation is used in a synthesis model.